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Related Experiment Video

Updated: Jun 18, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Ultrasound and Clinicopathological Features-Based Machine Learning Model for Predicting Neoadjuvant Therapy Efficacy

Tongtong Hao1, Xiaohui Ji1, Qianying Zhao1

  • 1Department of Ultrasound Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang City, Hebei Province, China.

Cancer Reports (Hoboken, N.J.)
|June 16, 2026
PubMed
Summary

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A new machine learning model accurately predicts pathological complete response (pCR) after neoadjuvant therapy (NAT) in breast cancer patients. This non-invasive tool integrates ultrasound imaging and clinical data, improving treatment planning.

Area of Science:

  • Oncology
  • Medical Imaging
  • Machine Learning

Background:

  • Predicting pathological complete response (pCR) after neoadjuvant therapy (NAT) in breast cancer is challenging.
  • Conventional imaging like ultrasound and MRI alone has limited accuracy for pCR prediction.

Purpose of the Study:

  • Develop and validate a machine learning model to predict pCR non-invasively.
  • Integrate ultrasound imaging features and clinicopathological data for individualized breast cancer treatment.

Main Methods:

  • Retrospective analysis of 609 breast cancer patients undergoing NAT.
  • Developed and validated Random Forest, Logistic Regression, and Support Vector Machine models.
  • Assessed model performance using ROC curves, decision analysis, SHAP, and feature importance.
Keywords:
breast cancermachine learningneoadjuvant therapyultrasound

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Last Updated: Jun 18, 2026

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Main Results:

  • The Random Forest model achieved an AUC of 0.85 and 84.7% accuracy, outperforming conventional imaging.
  • Key predictors identified: early NAT tumor volume reduction (≥80%), increased echogenicity, HER2 positivity, and tumor-infiltrating lymphocytes.
  • Ultrasound and MRI showed limited diagnostic performance as a baseline.

Conclusions:

  • The Random Forest model significantly enhances pCR prediction after NAT in breast cancer.
  • This practical, non-invasive tool can aid in personalized treatment planning and clinical decision-making.